SMF-SLAM: Sliding mode filtering based optimization algorithm for LiDAR SLAM

SMF-SLAM: Sliding mode filtering based optimization algorithm for LiDAR SLAM | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article SMF-SLAM: Sliding mode filtering based optimization algorithm for LiDAR SLAM Songlin Liu, Rongcan Qiao, Xinyu Lv This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9132984/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract In recent years, with the rapid development of LiDAR technology, there is an increasing demand for 3D change detection in automatic driving, infrastructure monitoring, and so on. Aiming at the research of LIDAR SLAM point cloud building model, this paper proposes an improved LIDAR SLAM system that combines sliding mode filtering and adaptive curvature thresholding method to improve the detection accuracy and efficiency. The adaptive threshold function based on historical threshold and current curvature change is designed to enhance the edge feature extraction accuracy by dynamically adjusting the threshold. Meanwhile, sliding mode filtering is used for local pose estimation to effectively improve the efficiency and accuracy of the system. Finally, a large number of experiments are conducted on the robot platform to verify the effectiveness of the algorithm. The experimental results show that the method can effectively improve the number of feature points extracted and the accuracy of map building in outdoor environments. SLAM sliding mode filtering adaptive threshold point cloud LiDAR Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers invited by journal 01 Apr, 2026 Editor invited by journal 20 Mar, 2026 Editor assigned by journal 17 Mar, 2026 Submission checks completed at journal 17 Mar, 2026 First submitted to journal 16 Mar, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9132984","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":616231157,"identity":"500444fd-2586-43ff-bffa-c0f28342c6cb","order_by":0,"name":"Songlin Liu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1UlEQVRIie3OsQrCMBCA4SuFdomdU0R9hZSAFPoyKUK7qHMHkbh0El3bt/ARCoVM0VlwsJtrRyexxUVEYkaH/HCQg/sgACbTf2Y1jOAx2FW/2FrEJm0WUnCYPnH8QmYx1ybepW6GgxynOzEQGLIo5u6xUhL/lBDakUUpvASDTGOOlkxJiEQw68nhup1iK69jjhH5Ser+Y0Sgjjz0iLUpJGYvwjWILx0KbYaDUiAaMpHSHM3VxJP27c7IeuIJFJzbVTTau1JNoHp/sG4c9f0nMZlMJtO3nk98QsI1jhisAAAAAElFTkSuQmCC","orcid":"","institution":"Collaborative Innovation Center for BeiDou Navigation Application","correspondingAuthor":true,"prefix":"","firstName":"Songlin","middleName":"","lastName":"Liu","suffix":""},{"id":616231158,"identity":"2cbeaaf3-b323-4f54-93c3-a8c46de0c156","order_by":1,"name":"Rongcan Qiao","email":"","orcid":"","institution":"Qufu Normal University","correspondingAuthor":false,"prefix":"","firstName":"Rongcan","middleName":"","lastName":"Qiao","suffix":""},{"id":616231159,"identity":"580624d5-bc49-4e7a-bf80-b051a02fc87d","order_by":2,"name":"Xinyu Lv","email":"","orcid":"","institution":"Qufu Normal University","correspondingAuthor":false,"prefix":"","firstName":"Xinyu","middleName":"","lastName":"Lv","suffix":""}],"badges":[],"createdAt":"2026-03-16 04:39:52","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9132984/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9132984/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106296989,"identity":"8bcdddbb-97e4-45b0-85c2-8ae5029c9958","added_by":"auto","created_at":"2026-04-07 08:43:58","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":498488,"visible":true,"origin":"","legend":"","description":"","filename":"SMFSLAMSlidingmodefiltering.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9132984/v1_covered_08fa3397-c1b3-4755-b585-2051fc8b0184.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"SMF-SLAM: Sliding mode filtering based optimization algorithm for LiDAR SLAM","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"discover-robotics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Robotics](https://link.springer.com/journal/44430)","snPcode":"44430","submissionUrl":"https://submission.springernature.com/new-submission/44430/3","title":"Discover Robotics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Unsupported Journal","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"SLAM, sliding mode filtering, adaptive threshold, point cloud, LiDAR","lastPublishedDoi":"10.21203/rs.3.rs-9132984/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9132984/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIn recent years, with the rapid development of LiDAR technology, there is an increasing demand for 3D change detection in automatic driving, infrastructure monitoring, and so on. Aiming at the research of LIDAR SLAM point cloud building model, this paper proposes an improved LIDAR SLAM system that combines sliding mode filtering and adaptive curvature thresholding method to improve the detection accuracy and efficiency. The adaptive threshold function based on historical threshold and current curvature change is designed to enhance the edge feature extraction accuracy by dynamically adjusting the threshold. Meanwhile, sliding mode filtering is used for local pose estimation to effectively improve the efficiency and accuracy of the system. Finally, a large number of experiments are conducted on the robot platform to verify the effectiveness of the algorithm. 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